PyRadiomics toolbox in Python¶¶
In [1]:
import os, zipfile #create, read, write, append, and list a ZIP file
import pandas as pd #data analysis tools
import seaborn as sns #statistical data visualization
from six.moves import urllib #Copy a network object denoted by a URL to a local file
from matplotlib import pyplot as plt
url = "http://www.spl.harvard.edu/publications/bitstream/download/5270"
filename = 'example_data/Tumorbase.zip'
if not os.path.isfile(filename):
if not os.path.isdir('example_data'):
os.mkdir('example_data')
print ("retrieving")
urllib.request.urlretrieve(url, filename)
else:
print ("file already downloaded")
extracted_path = 'example_data/tumorbase'
if not os.path.isdir(extracted_path):
print ("unzipping")
z = zipfile.ZipFile(filename)
z.extractall('example_data')
print ("done unzipping")
file already downloaded
1) Setting up data¶
In [2]:
import os
import SimpleITK as sitk
# Radiomics package
from radiomics import featureextractor
import six, numpy as np
from radiomics import getTestCase
imagepath_1, labelpath_1 = getTestCase('brain1')
imagepath_2, labelpath_2 = getTestCase('brain2')
image_1 = sitk.ReadImage(imagepath_1)
label_1 = sitk.ReadImage(labelpath_1)
image_2 = sitk.ReadImage(imagepath_2)
label_2 = sitk.ReadImage(labelpath_2)
imagepath_1
Out[2]:
'/tmp/pyradiomics/data/brain1_image.nrrd'
2) Show the images¶
In [3]:
%matplotlib inline
import matplotlib.pyplot as plt
plt.figure(figsize=(20,20))
# First image
plt.subplot(2,2,1)
plt.imshow(sitk.GetArrayFromImage(image_1)[12,:,:], cmap="gray")
plt.title("Brain #1")
plt.subplot(2,2,2)
plt.imshow(sitk.GetArrayFromImage(label_1)[12,:,:])
plt.title("Segmentation #1")
# Second image
plt.subplot(2,2,3)
plt.imshow(sitk.GetArrayFromImage(image_2)[8,:,:], cmap="gray")
plt.title("Brain #2")
plt.subplot(2,2,4)
plt.imshow(sitk.GetArrayFromImage(label_2)[8,:,:])
plt.title("Segmentation #2")
plt.show()
3) Extract the features¶
In [4]:
import os
# Instantiate the extractor
params = os.path.join(os.getcwd(), '..', 'examples', 'exampleSettings', 'Params.yaml')
extractor = featureextractor.RadiomicsFeaturesExtractor(params)
result_1 = extractor.execute(image_1, label_1)
result_2 = extractor.execute(image_2, label_2)
GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated
In [36]:
resk=list(result_2.keys())
resv=list(result_2.values())
df=pd.DataFrame([resk,resv]).transpose()
df.columns=['Features','Values']
df
Out[36]:
| Features | Values | |
|---|---|---|
| 0 | diagnostics_Versions_PyRadiomics | 2.1.2 |
| 1 | diagnostics_Versions_Numpy | 1.16.2 |
| 2 | diagnostics_Versions_SimpleITK | 1.2.0 |
| 3 | diagnostics_Versions_PyWavelet | 1.0.0 |
| 4 | diagnostics_Versions_Python | 3.6.8 |
| 5 | diagnostics_Configuration_Settings | {'minimumROIDimensions': 2, 'minimumROISize': ... |
| 6 | diagnostics_Configuration_EnabledImageTypes | {'Original': {}} |
| 7 | diagnostics_Image-original_Hash | f2b8fbc4d5d1da08a1a70e79a301f8a830139438 |
| 8 | diagnostics_Image-original_Spacing | (0.7812499999999999, 0.7812499999999999, 6.499... |
| 9 | diagnostics_Image-original_Size | (256, 256, 23) |
| 10 | diagnostics_Image-original_Mean | 236.202 |
| 11 | diagnostics_Image-original_Minimum | 0 |
| 12 | diagnostics_Image-original_Maximum | 1523 |
| 13 | diagnostics_Mask-original_Hash | b41049c71633e194bee4891750392b72eabd8800 |
| 14 | diagnostics_Mask-original_Spacing | (0.7812499999999999, 0.7812499999999999, 6.499... |
| 15 | diagnostics_Mask-original_Size | (256, 256, 23) |
| 16 | diagnostics_Mask-original_BoundingBox | (205, 155, 8, 20, 15, 3) |
| 17 | diagnostics_Mask-original_VoxelNum | 453 |
| 18 | diagnostics_Mask-original_VolumeNum | 1 |
| 19 | diagnostics_Mask-original_CenterOfMassIndex | (215.21192052980132, 161.45474613686534, 8.589... |
| 20 | diagnostics_Mask-original_CenterOfMass | (58.134312913907266, -22.717773471183904, -6.8... |
| 21 | original_shape_Elongation | 0.740769 |
| 22 | original_shape_Flatness | 0.618816 |
| 23 | original_shape_LeastAxisLength | 11.1802 |
| 24 | original_shape_MajorAxisLength | 18.0672 |
| 25 | original_shape_Maximum2DDiameterColumn | 23.5933 |
| 26 | original_shape_Maximum2DDiameterRow | 20.7289 |
| 27 | original_shape_Maximum2DDiameterSlice | 16.313 |
| 28 | original_shape_Maximum3DDiameter | 24.4445 |
| 29 | original_shape_MeshVolume | 1736.02 |
| ... | ... | ... |
| 98 | original_glrlm_LowGrayLevelRunEmphasis | 0.0076064 |
| 99 | original_glrlm_RunEntropy | 4.20976 |
| 100 | original_glrlm_RunLengthNonUniformity | 365.075 |
| 101 | original_glrlm_RunLengthNonUniformityNormalized | 0.866363 |
| 102 | original_glrlm_RunPercentage | 0.918152 |
| 103 | original_glrlm_RunVariance | 0.150366 |
| 104 | original_glrlm_ShortRunEmphasis | 0.940657 |
| 105 | original_glrlm_ShortRunHighGrayLevelEmphasis | 266.862 |
| 106 | original_glrlm_ShortRunLowGrayLevelEmphasis | 0.00732226 |
| 107 | original_glszm_GrayLevelNonUniformity | 9.63804 |
| 108 | original_glszm_GrayLevelNonUniformityNormalized | 0.0591291 |
| 109 | original_glszm_GrayLevelVariance | 27.8504 |
| 110 | original_glszm_HighGrayLevelZoneEmphasis | 319.988 |
| 111 | original_glszm_LargeAreaEmphasis | 35.3681 |
| 112 | original_glszm_LargeAreaHighGrayLevelEmphasis | 7639.02 |
| 113 | original_glszm_LargeAreaLowGrayLevelEmphasis | 0.184659 |
| 114 | original_glszm_LowGrayLevelZoneEmphasis | 0.0117436 |
| 115 | original_glszm_SizeZoneNonUniformity | 69.184 |
| 116 | original_glszm_SizeZoneNonUniformityNormalized | 0.424442 |
| 117 | original_glszm_SmallAreaEmphasis | 0.677063 |
| 118 | original_glszm_SmallAreaHighGrayLevelEmphasis | 218.879 |
| 119 | original_glszm_SmallAreaLowGrayLevelEmphasis | 0.00988395 |
| 120 | original_glszm_ZoneEntropy | 5.67111 |
| 121 | original_glszm_ZonePercentage | 0.359823 |
| 122 | original_glszm_ZoneVariance | 27.6445 |
| 123 | original_ngtdm_Busyness | 0.151645 |
| 124 | original_ngtdm_Coarseness | 0.0128616 |
| 125 | original_ngtdm_Complexity | 762.182 |
| 126 | original_ngtdm_Contrast | 0.121837 |
| 127 | original_ngtdm_Strength | 3.96631 |
128 rows × 2 columns
4) Prepare for plotting¶
In [37]:
feature_1 = np.array([])
feature_2 = np.array([])
for key, value in six.iteritems(result_1):
print(key)
if key.startswith("original_"):
feature_1 = np.append ( feature_1, result_1[key])
feature_2 = np.append ( feature_2, result_2[key])
result_1['original_glszm_ZoneVariance']
diagnostics_Versions_PyRadiomics diagnostics_Versions_Numpy diagnostics_Versions_SimpleITK diagnostics_Versions_PyWavelet diagnostics_Versions_Python diagnostics_Configuration_Settings diagnostics_Configuration_EnabledImageTypes diagnostics_Image-original_Hash diagnostics_Image-original_Spacing diagnostics_Image-original_Size diagnostics_Image-original_Mean diagnostics_Image-original_Minimum diagnostics_Image-original_Maximum diagnostics_Mask-original_Hash diagnostics_Mask-original_Spacing diagnostics_Mask-original_Size diagnostics_Mask-original_BoundingBox diagnostics_Mask-original_VoxelNum diagnostics_Mask-original_VolumeNum diagnostics_Mask-original_CenterOfMassIndex diagnostics_Mask-original_CenterOfMass original_shape_Elongation original_shape_Flatness original_shape_LeastAxisLength original_shape_MajorAxisLength original_shape_Maximum2DDiameterColumn original_shape_Maximum2DDiameterRow original_shape_Maximum2DDiameterSlice original_shape_Maximum3DDiameter original_shape_MeshVolume original_shape_MinorAxisLength original_shape_Sphericity original_shape_SurfaceArea original_shape_SurfaceVolumeRatio original_shape_VoxelVolume original_firstorder_10Percentile original_firstorder_90Percentile original_firstorder_Energy original_firstorder_Entropy original_firstorder_InterquartileRange original_firstorder_Kurtosis original_firstorder_Maximum original_firstorder_MeanAbsoluteDeviation original_firstorder_Mean original_firstorder_Median original_firstorder_Minimum original_firstorder_Range original_firstorder_RobustMeanAbsoluteDeviation original_firstorder_RootMeanSquared original_firstorder_Skewness original_firstorder_TotalEnergy original_firstorder_Uniformity original_firstorder_Variance original_glcm_Autocorrelation original_glcm_ClusterProminence original_glcm_ClusterShade original_glcm_ClusterTendency original_glcm_Contrast original_glcm_Correlation original_glcm_DifferenceAverage original_glcm_DifferenceEntropy original_glcm_DifferenceVariance original_glcm_Id original_glcm_Idm original_glcm_Idmn original_glcm_Idn original_glcm_Imc1 original_glcm_Imc2 original_glcm_InverseVariance original_glcm_JointAverage original_glcm_JointEnergy original_glcm_JointEntropy original_glcm_MCC original_glcm_MaximumProbability original_glcm_SumAverage original_glcm_SumEntropy original_glcm_SumSquares original_gldm_DependenceEntropy original_gldm_DependenceNonUniformity original_gldm_DependenceNonUniformityNormalized original_gldm_DependenceVariance original_gldm_GrayLevelNonUniformity original_gldm_GrayLevelVariance original_gldm_HighGrayLevelEmphasis original_gldm_LargeDependenceEmphasis original_gldm_LargeDependenceHighGrayLevelEmphasis original_gldm_LargeDependenceLowGrayLevelEmphasis original_gldm_LowGrayLevelEmphasis original_gldm_SmallDependenceEmphasis original_gldm_SmallDependenceHighGrayLevelEmphasis original_gldm_SmallDependenceLowGrayLevelEmphasis original_glrlm_GrayLevelNonUniformity original_glrlm_GrayLevelNonUniformityNormalized original_glrlm_GrayLevelVariance original_glrlm_HighGrayLevelRunEmphasis original_glrlm_LongRunEmphasis original_glrlm_LongRunHighGrayLevelEmphasis original_glrlm_LongRunLowGrayLevelEmphasis original_glrlm_LowGrayLevelRunEmphasis original_glrlm_RunEntropy original_glrlm_RunLengthNonUniformity original_glrlm_RunLengthNonUniformityNormalized original_glrlm_RunPercentage original_glrlm_RunVariance original_glrlm_ShortRunEmphasis original_glrlm_ShortRunHighGrayLevelEmphasis original_glrlm_ShortRunLowGrayLevelEmphasis original_glszm_GrayLevelNonUniformity original_glszm_GrayLevelNonUniformityNormalized original_glszm_GrayLevelVariance original_glszm_HighGrayLevelZoneEmphasis original_glszm_LargeAreaEmphasis original_glszm_LargeAreaHighGrayLevelEmphasis original_glszm_LargeAreaLowGrayLevelEmphasis original_glszm_LowGrayLevelZoneEmphasis original_glszm_SizeZoneNonUniformity original_glszm_SizeZoneNonUniformityNormalized original_glszm_SmallAreaEmphasis original_glszm_SmallAreaHighGrayLevelEmphasis original_glszm_SmallAreaLowGrayLevelEmphasis original_glszm_ZoneEntropy original_glszm_ZonePercentage original_glszm_ZoneVariance original_ngtdm_Busyness original_ngtdm_Coarseness original_ngtdm_Complexity original_ngtdm_Contrast original_ngtdm_Strength
Out[37]:
8.721239097486347
In [43]:
feature_2[:3]
len(feature_1)
Out[43]:
107
5) Plot¶
In [39]:
plt.figure(figsize=(20,20))
plt.subplot(3,1,1)
plt.plot(feature_1)
plt.yscale('log')
plt.title ( "Features from brain #1")
plt.subplot(3,1,2)
plt.plot(feature_2)
plt.yscale('log')
plt.title ( "Features from brain #1")
plt.subplot(3,1,3)
plt.plot(feature_1 - feature_2)
plt.yscale('log')
plt.title ( "Difference")
plt.show()
6) Make a numpy array of all the values and correlation¶
In [44]:
feature_names1 = list(sorted(filter ( lambda k: k.startswith("original_"), result_1 )))
feature_names2 = list(sorted(filter ( lambda k: k.startswith("original_"), result_2 )))
display(feature_names1[:10])
import numpy as np
feature_values1=[]
feature_values2=[]
samples = np.zeros((10,len(feature_names1)))
for feature_name in feature_names1:
feature_values1 = np.append(feature_values1, result_1[feature_name])
feature_values2 = np.append(feature_values2, result_2[feature_name])
import pandas as pd
result=pd.DataFrame([feature_values1,feature_values2])
result.columns=feature_names1
result
['original_firstorder_10Percentile', 'original_firstorder_90Percentile', 'original_firstorder_Energy', 'original_firstorder_Entropy', 'original_firstorder_InterquartileRange', 'original_firstorder_Kurtosis', 'original_firstorder_Maximum', 'original_firstorder_Mean', 'original_firstorder_MeanAbsoluteDeviation', 'original_firstorder_Median']
Out[44]:
| original_firstorder_10Percentile | original_firstorder_90Percentile | original_firstorder_Energy | original_firstorder_Entropy | original_firstorder_InterquartileRange | original_firstorder_Kurtosis | original_firstorder_Maximum | original_firstorder_Mean | original_firstorder_MeanAbsoluteDeviation | original_firstorder_Median | ... | original_shape_Maximum2DDiameterColumn | original_shape_Maximum2DDiameterRow | original_shape_Maximum2DDiameterSlice | original_shape_Maximum3DDiameter | original_shape_MeshVolume | original_shape_MinorAxisLength | original_shape_Sphericity | original_shape_SurfaceArea | original_shape_SurfaceVolumeRatio | original_shape_VoxelVolume | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 632.0 | 1044.4 | 2.918821e+09 | 4.601936 | 253.0 | 2.180773 | 1266.0 | 825.235436 | 133.447262 | 812.0 | ... | 49.490855 | 65.889060 | 53.593978 | 69.600990 | 16147.511800 | 34.849702 | 0.479823 | 6438.821604 | 0.398750 | 16412.658691 |
| 1 | 305.2 | 532.6 | 7.390377e+07 | 3.820600 | 97.0 | 4.674716 | 729.0 | 390.785872 | 73.554269 | 375.0 | ... | 23.593253 | 20.728928 | 16.312979 | 24.444532 | 1736.017863 | 13.383590 | 0.751304 | 929.760151 | 0.535571 | 1797.180176 |
2 rows × 107 columns
In [45]:
import seaborn as sns
# Construct a pandas dataframe from the samples
#d = pd.DataFrame(data=samples, columns=feature_names)
corr = result.corr()
# Set up the matplotlib figure, make it big!
f, ax = plt.subplots(figsize=(15, 10))
# Draw the heatmap using seaborn
sns.heatmap(corr, vmax=.8, square=True)
plt.show()
II) Extract features¶
In [46]:
import SimpleITK as sitk
from radiomics import featureextractor
# Load up the segmentations, 1 to 10 and extract the features
params = os.path.join(os.getcwd(), '..', 'examples', 'exampleSettings', 'Params.yaml')
extractor = featureextractor.RadiomicsFeaturesExtractor(params)
# hang on to all our features
features = {}
for case_id in range(1,11):
path = 'example_data/tumorbase/AutomatedSegmentation/case{}/'.format(case_id)
image = sitk.ReadImage(path + "grayscale.nrrd")
mask = sitk.ReadImage(path + "segmented.nrrd")
# Tumor is in label value 6
features[case_id] = extractor.execute ( image, mask, label=6 )
# A list of the valid features, sorted
feature_names = list(sorted(filter ( lambda k: k.startswith("original_"), features[1] )))
feature_names[:10]
GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated
Out[46]:
['original_firstorder_10Percentile', 'original_firstorder_90Percentile', 'original_firstorder_Energy', 'original_firstorder_Entropy', 'original_firstorder_InterquartileRange', 'original_firstorder_Kurtosis', 'original_firstorder_Maximum', 'original_firstorder_Mean', 'original_firstorder_MeanAbsoluteDeviation', 'original_firstorder_Median']
In [47]:
features
Out[47]:
{1: OrderedDict([('diagnostics_Versions_PyRadiomics', '2.1.2'),
('diagnostics_Versions_Numpy', '1.16.2'),
('diagnostics_Versions_SimpleITK', '1.2.0'),
('diagnostics_Versions_PyWavelet', '1.0.0'),
('diagnostics_Versions_Python', '3.6.8'),
('diagnostics_Configuration_Settings',
{'minimumROIDimensions': 2,
'minimumROISize': None,
'normalize': False,
'normalizeScale': 1,
'removeOutliers': None,
'resampledPixelSpacing': None,
'interpolator': 'sitkBSpline',
'preCrop': False,
'padDistance': 5,
'distances': [1],
'force2D': False,
'force2Ddimension': 0,
'resegmentRange': None,
'label': 6,
'additionalInfo': True}),
('diagnostics_Configuration_EnabledImageTypes',
{'Original': {}}),
('diagnostics_Image-original_Hash',
'4b422fa3af4c0b3c006422d6cac17821f98ebda2'),
('diagnostics_Image-original_Spacing', (0.9375, 0.9375, 1.5)),
('diagnostics_Image-original_Size', (256, 256, 123)),
('diagnostics_Image-original_Mean', 39.73005862352325),
('diagnostics_Image-original_Minimum', 0.0),
('diagnostics_Image-original_Maximum', 833.0),
('diagnostics_Mask-original_Hash',
'9d7b71692f7c9fe934523b67d82ebe6f97d1ce86'),
('diagnostics_Mask-original_Spacing', (0.9375, 0.9375, 1.5)),
('diagnostics_Mask-original_Size', (256, 256, 123)),
('diagnostics_Mask-original_BoundingBox',
(103, 168, 35, 54, 42, 28)),
('diagnostics_Mask-original_VoxelNum', 28381),
('diagnostics_Mask-original_VolumeNum', 1),
('diagnostics_Mask-original_CenterOfMassIndex',
(129.5908882703217, 189.75744335999437, 49.803706705190095)),
('diagnostics_Mask-original_CenterOfMass',
(17.544439942214865, 1.9604577534265815, 58.36660314999472)),
('original_shape_Elongation', 0.794856283000445),
('original_shape_Flatness', 0.5829014701298105),
('original_shape_LeastAxisLength', 28.585273833005722),
('original_shape_MajorAxisLength', 49.03963242130761),
('original_shape_Maximum2DDiameterColumn', 51.70242378264292),
('original_shape_Maximum2DDiameterRow', 45.621104285736),
('original_shape_Maximum2DDiameterSlice', 58.245842126284),
('original_shape_Maximum3DDiameter', 58.73540377191596),
('original_shape_MeshVolume', 37283.367919921875),
('original_shape_MinorAxisLength', 38.979459946108676),
('original_shape_Sphericity', 0.6598900382284406),
('original_shape_SurfaceArea', 8178.794757930033),
('original_shape_SurfaceVolumeRatio', 0.2193684533944639),
('original_shape_VoxelVolume', 37416.357421875),
('original_firstorder_10Percentile', 140.0),
('original_firstorder_90Percentile', 175.0),
('original_firstorder_Energy', 703288387.0),
('original_firstorder_Entropy', 1.407119095841021),
('original_firstorder_InterquartileRange', 18.0),
('original_firstorder_Kurtosis', 5.772689236689695),
('original_firstorder_Maximum', 291.0),
('original_firstorder_MeanAbsoluteDeviation',
10.982512664874108),
('original_firstorder_Mean', 156.77040978119163),
('original_firstorder_Median', 155.0),
('original_firstorder_Minimum', 66.0),
('original_firstorder_Range', 225.0),
('original_firstorder_RobustMeanAbsoluteDeviation',
7.6494640117776225),
('original_firstorder_RootMeanSquared', 157.41745118471903),
('original_firstorder_Skewness', 0.7568529462163773),
('original_firstorder_TotalEnergy', 927186838.3300781),
('original_firstorder_Uniformity', 0.43976272587212006),
('original_firstorder_Variance', 203.2925545306454),
('original_glcm_Autocorrelation', 23.02086232451176),
('original_glcm_ClusterProminence', 5.2274395746651035),
('original_glcm_ClusterShade', 0.5363520463071897),
('original_glcm_ClusterTendency', 1.0646752659349863),
('original_glcm_Contrast', 0.6042855954860485),
('original_glcm_Correlation', 0.27504610069981744),
('original_glcm_DifferenceAverage', 0.5085697614251752),
('original_glcm_DifferenceEntropy', 1.1996914930109133),
('original_glcm_DifferenceVariance', 0.3430553610135786),
('original_glcm_Id', 0.7601740475258996),
('original_glcm_Idm', 0.7551027250539343),
('original_glcm_Idmn', 0.9940961816677637),
('original_glcm_Idn', 0.9544678934763919),
('original_glcm_Imc1', -0.051228352724291396),
('original_glcm_Imc2', 0.3425366377362223),
('original_glcm_InverseVariance', 0.437644226953438),
('original_glcm_JointAverage', 4.785995154182346),
('original_glcm_JointEnergy', 0.21155483301820516),
('original_glcm_JointEntropy', 2.7082121788624534),
('original_glcm_MCC', 0.3434195543527093),
('original_glcm_MaximumProbability', 0.36680411286664766),
('original_glcm_SumAverage', 9.571990308364692),
('original_glcm_SumEntropy', 2.0400770221007183),
('original_glcm_SumSquares', 0.4172402153552586),
('original_gldm_DependenceEntropy', 5.651024217126519),
('original_gldm_DependenceNonUniformity', 1325.3549557802755),
('original_gldm_DependenceNonUniformityNormalized',
0.046698670088449155),
('original_gldm_DependenceVariance', 33.861326489794834),
('original_gldm_GrayLevelNonUniformity', 12480.905922976639),
('original_gldm_GrayLevelVariance', 0.42822779811730444),
('original_gldm_HighGrayLevelEmphasis', 23.398329868574045),
('original_gldm_LargeDependenceEmphasis', 224.39586342975934),
('original_gldm_LargeDependenceHighGrayLevelEmphasis',
5210.004298650506),
('original_gldm_LargeDependenceLowGrayLevelEmphasis',
10.079057835608813),
('original_gldm_LowGrayLevelEmphasis', 0.046066072680411414),
('original_gldm_SmallDependenceEmphasis', 0.023471854578384868),
('original_gldm_SmallDependenceHighGrayLevelEmphasis',
0.6209139175643873),
('original_gldm_SmallDependenceLowGrayLevelEmphasis',
0.0011797683485162763),
('original_glrlm_GrayLevelNonUniformity', 5361.074859168112),
('original_glrlm_GrayLevelNonUniformityNormalized',
0.37206078398825027),
('original_glrlm_GrayLevelVariance', 0.5780095826636417),
('original_glrlm_HighGrayLevelRunEmphasis', 23.63648816520193),
('original_glrlm_LongRunEmphasis', 6.712932378878997),
('original_glrlm_LongRunHighGrayLevelEmphasis',
157.0801381458293),
('original_glrlm_LongRunLowGrayLevelEmphasis',
0.3016840208223139),
('original_glrlm_LowGrayLevelRunEmphasis', 0.046703187080956984),
('original_glrlm_RunEntropy', 3.476352318156401),
('original_glrlm_RunLengthNonUniformity', 5465.472667273132),
('original_glrlm_RunLengthNonUniformityNormalized',
0.3758276552552058),
('original_glrlm_RunPercentage', 0.5075605835973688),
('original_glrlm_RunVariance', 2.7168923119446124),
('original_glrlm_ShortRunEmphasis', 0.6299285666540271),
('original_glrlm_ShortRunHighGrayLevelEmphasis',
15.114353516662515),
('original_glrlm_ShortRunLowGrayLevelEmphasis',
0.02960247888830149),
('original_glszm_GrayLevelNonUniformity', 111.29951690821257),
('original_glszm_GrayLevelNonUniformityNormalized',
0.2688394128217695),
('original_glszm_GrayLevelVariance', 2.3197099115498614),
('original_glszm_HighGrayLevelZoneEmphasis', 29.818840579710145),
('original_glszm_LargeAreaEmphasis', 833747.4710144928),
('original_glszm_LargeAreaHighGrayLevelEmphasis',
19240088.371980675),
('original_glszm_LargeAreaLowGrayLevelEmphasis',
37436.11992709045),
('original_glszm_LowGrayLevelZoneEmphasis',
0.051512351004098185),
('original_glszm_SizeZoneNonUniformity', 158.12077294685992),
('original_glszm_SizeZoneNonUniformityNormalized',
0.3819342341711592),
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0.143902025910503),
('original_glszm_GrayLevelVariance', 5.280511558926566),
('original_glszm_HighGrayLevelZoneEmphasis', 31.442073170731707),
('original_glszm_LargeAreaEmphasis', 56852.05386178862),
('original_glszm_LargeAreaHighGrayLevelEmphasis',
2931447.1788617885),
('original_glszm_LargeAreaLowGrayLevelEmphasis',
1156.8921826083727),
('original_glszm_LowGrayLevelZoneEmphasis', 0.07838788722751112),
('original_glszm_SizeZoneNonUniformity', 290.7743902439024),
('original_glszm_SizeZoneNonUniformityNormalized',
0.29550242910965696),
('original_glszm_SmallAreaEmphasis', 0.5604644690421469),
('original_glszm_SmallAreaHighGrayLevelEmphasis',
17.61193874232189),
('original_glszm_SmallAreaLowGrayLevelEmphasis',
0.0474083490072353),
('original_glszm_ZoneEntropy', 5.485732273732717),
('original_glszm_ZonePercentage', 0.05387352860662469),
('original_glszm_ZoneVariance', 56507.5063196097),
('original_ngtdm_Busyness', 23.245251276221417),
('original_ngtdm_Coarseness', 0.0004825537269538527),
('original_ngtdm_Complexity', 37.85597455257226),
('original_ngtdm_Contrast', 0.035694284374923474),
('original_ngtdm_Strength', 0.020623856827336924)]),
10: OrderedDict([('diagnostics_Versions_PyRadiomics', '2.1.2'),
('diagnostics_Versions_Numpy', '1.16.2'),
('diagnostics_Versions_SimpleITK', '1.2.0'),
('diagnostics_Versions_PyWavelet', '1.0.0'),
('diagnostics_Versions_Python', '3.6.8'),
('diagnostics_Configuration_Settings',
{'minimumROIDimensions': 2,
'minimumROISize': None,
'normalize': False,
'normalizeScale': 1,
'removeOutliers': None,
'resampledPixelSpacing': None,
'interpolator': 'sitkBSpline',
'preCrop': False,
'padDistance': 5,
'distances': [1],
'force2D': False,
'force2Ddimension': 0,
'resegmentRange': None,
'label': 6,
'additionalInfo': True}),
('diagnostics_Configuration_EnabledImageTypes',
{'Original': {}}),
('diagnostics_Image-original_Hash',
'35f1580c86e11e0a4463c760b1a1f771982fa496'),
('diagnostics_Image-original_Spacing', (0.9375, 0.9375, 1.5)),
('diagnostics_Image-original_Size', (256, 256, 123)),
('diagnostics_Image-original_Mean', 38.50025729047574),
('diagnostics_Image-original_Minimum', 0.0),
('diagnostics_Image-original_Maximum', 792.0),
('diagnostics_Mask-original_Hash',
'f89deb69860d0c151f57f8797191e449c15ee27b'),
('diagnostics_Mask-original_Spacing', (0.9375, 0.9375, 1.5)),
('diagnostics_Mask-original_Size', (256, 256, 123)),
('diagnostics_Mask-original_BoundingBox',
(105, 86, 22, 75, 55, 30)),
('diagnostics_Mask-original_VoxelNum', 33502),
('diagnostics_Mask-original_VolumeNum', 1),
('diagnostics_Mask-original_CenterOfMassIndex',
(143.44071995701748, 111.29457942809384, 38.03799773147872)),
('diagnostics_Mask-original_CenterOfMass',
(35.193003402781926, 14.944674959703889, -15.192331786162029)),
('original_shape_Elongation', 0.5825062842587055),
('original_shape_Flatness', 0.4507379954185646),
('original_shape_LeastAxisLength', 28.938898690625127),
('original_shape_MajorAxisLength', 64.20337088235011),
('original_shape_Maximum2DDiameterColumn', 68.58528563948684),
('original_shape_Maximum2DDiameterRow', 49.50142043416532),
('original_shape_Maximum2DDiameterSlice', 71.2746667828058),
('original_shape_Maximum3DDiameter', 75.79848386511435),
('original_shape_MeshVolume', 44058.251953125),
('original_shape_MinorAxisLength', 37.398867009561336),
('original_shape_Sphericity', 0.5456363057624773),
('original_shape_SurfaceArea', 11056.025695975895),
('original_shape_SurfaceVolumeRatio', 0.25094108835136625),
('original_shape_VoxelVolume', 44167.67578125),
('original_firstorder_10Percentile', 48.0),
('original_firstorder_90Percentile', 81.0),
('original_firstorder_Energy', 143734129.0),
('original_firstorder_Entropy', 1.340339712993897),
('original_firstorder_InterquartileRange', 17.0),
('original_firstorder_Kurtosis', 6.272806104662042),
('original_firstorder_Maximum', 252.0),
('original_firstorder_MeanAbsoluteDeviation',
10.760356983847393),
('original_firstorder_Mean', 63.95101784968062),
('original_firstorder_Median', 63.0),
('original_firstorder_Minimum', 0.0),
('original_firstorder_Range', 252.0),
('original_firstorder_RobustMeanAbsoluteDeviation',
7.211091998282539),
('original_firstorder_RootMeanSquared', 65.50049524029401),
('original_firstorder_Skewness', 0.49756071259781126),
('original_firstorder_TotalEnergy', 189493236.47460938),
('original_firstorder_Uniformity', 0.4995326982582334),
('original_firstorder_Variance', 200.58219271360892),
('original_glcm_Autocorrelation', 9.328989598930708),
('original_glcm_ClusterProminence', 3.7629784821130325),
('original_glcm_ClusterShade', 0.18939743667708436),
('original_glcm_ClusterTendency', 0.9122758953194448),
('original_glcm_Contrast', 0.433299681559846),
('original_glcm_Correlation', 0.3543879611134286),
('original_glcm_DifferenceAverage', 0.3824281995386356),
('original_glcm_DifferenceEntropy', 1.0588296124536536),
('original_glcm_DifferenceVariance', 0.2847943492494132),
('original_glcm_Id', 0.816503174342922),
('original_glcm_Idm', 0.8137608790919745),
('original_glcm_Idmn', 0.9964788013733197),
('original_glcm_Idn', 0.9684469827398222),
('original_glcm_Imc1', -0.09185235861809793),
('original_glcm_Imc2', 0.4315021657555729),
('original_glcm_InverseVariance', 0.3443315038368407),
('original_glcm_JointAverage', 3.0346597601429717),
('original_glcm_JointEnergy', 0.3152003621809232),
('original_glcm_JointEntropy', 2.384611209692664),
('original_glcm_MCC', 0.3731486518544802),
('original_glcm_MaximumProbability', 0.5299275567588405),
('original_glcm_SumAverage', 6.069319520285941),
('original_glcm_SumEntropy', 1.898465524023218),
('original_glcm_SumSquares', 0.3363938942198226),
('original_gldm_DependenceEntropy', 5.49398634173834),
('original_gldm_DependenceNonUniformity', 1404.051041728852),
('original_gldm_DependenceNonUniformityNormalized',
0.041909469337020236),
('original_gldm_DependenceVariance', 49.757052774248194),
('original_gldm_GrayLevelNonUniformity', 16735.34445704734),
('original_gldm_GrayLevelVariance', 0.38663444904191635),
('original_gldm_HighGrayLevelEmphasis', 9.88164885678467),
('original_gldm_LargeDependenceEmphasis', 310.1542594471972),
('original_gldm_LargeDependenceHighGrayLevelEmphasis',
2834.444242134798),
('original_gldm_LargeDependenceLowGrayLevelEmphasis',
35.94942035132654),
('original_gldm_LowGrayLevelEmphasis', 0.12220355166615725),
('original_gldm_SmallDependenceEmphasis', 0.02007727369335469),
('original_gldm_SmallDependenceHighGrayLevelEmphasis',
0.275158415569856),
('original_gldm_SmallDependenceLowGrayLevelEmphasis',
0.0030507609365585425),
('original_glrlm_GrayLevelNonUniformity', 4770.358532796219),
('original_glrlm_GrayLevelNonUniformityNormalized',
0.340363608382969),
('original_glrlm_GrayLevelVariance', 0.662228609242522),
('original_glrlm_HighGrayLevelRunEmphasis', 10.702721366789941),
('original_glrlm_LongRunEmphasis', 12.557228953336582),
('original_glrlm_LongRunHighGrayLevelEmphasis',
116.87335887791674),
('original_glrlm_LongRunLowGrayLevelEmphasis',
1.4541482477567451),
('original_glrlm_LowGrayLevelRunEmphasis', 0.12854891070459382),
('original_glrlm_RunEntropy', 3.7927797235122473),
('original_glrlm_RunLengthNonUniformity', 4767.087809116933),
('original_glrlm_RunLengthNonUniformityNormalized',
0.33498316598211225),
('original_glrlm_RunPercentage', 0.41781432107382793),
('original_glrlm_RunVariance', 6.453790920101132),
('original_glrlm_ShortRunEmphasis', 0.5940185742277792),
('original_glrlm_ShortRunHighGrayLevelEmphasis',
6.8231279946783445),
('original_glrlm_ShortRunLowGrayLevelEmphasis',
0.07868870925795055),
('original_glszm_GrayLevelNonUniformity', 120.9375),
('original_glszm_GrayLevelNonUniformityNormalized',
0.2699497767857143),
('original_glszm_GrayLevelVariance', 2.4907475685586733),
('original_glszm_HighGrayLevelZoneEmphasis', 15.229910714285714),
('original_glszm_LargeAreaEmphasis', 1224494.138392857),
('original_glszm_LargeAreaHighGrayLevelEmphasis',
11474672.457589285),
('original_glszm_LargeAreaLowGrayLevelEmphasis',
135031.32683915953),
('original_glszm_LowGrayLevelZoneEmphasis', 0.1846119770030755),
('original_glszm_SizeZoneNonUniformity', 135.60714285714286),
('original_glszm_SizeZoneNonUniformityNormalized',
0.30269451530612246),
('original_glszm_SmallAreaEmphasis', 0.5656454742125366),
('original_glszm_SmallAreaHighGrayLevelEmphasis',
9.20637049344557),
('original_glszm_SmallAreaLowGrayLevelEmphasis',
0.09538076016235757),
('original_glszm_ZoneEntropy', 4.486520421879557),
('original_glszm_ZonePercentage', 0.013372335979941497),
('original_glszm_ZoneVariance', 1218901.9030412945),
('original_ngtdm_Busyness', 89.00970507751772),
('original_ngtdm_Coarseness', 0.00025931753874922265),
('original_ngtdm_Complexity', 19.569720812548336),
('original_ngtdm_Contrast', 0.0037167286858861277),
('original_ngtdm_Strength', 0.02891704912500508)])}
III) Show images and labels¶
In [48]:
case_id=2
path = 'example_data/tumorbase/AutomatedSegmentation/case{}/'.format(case_id)
im = sitk.ReadImage(path + "grayscale.nrrd")
mk = sitk.ReadImage(path + "segmented.nrrd")
image=sitk.GetArrayFromImage(im)
mask=sitk.GetArrayFromImage(mk)
print(image.shape)
for i in range(image.shape[0]-1):
plt.figure(figsize=(8,8))
plt.subplot(1,2,1)
plt.title('Image')
plt.imshow(image[i,:,:], cmap="gray")
plt.subplot(1,2,2)
plt.title('Mask')
plt.imshow(mask[i,:,:], cmap="gray")
(123, 256, 256)
/home/mrclnndrd/anaconda3/envs/pyradiomics/lib/python3.6/site-packages/matplotlib/pyplot.py:514: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). max_open_warning, RuntimeWarning)
IV) Make a numpy array of all the values¶
In [49]:
import numpy as np
samples = np.zeros((10,len(feature_names)))
for case_id in range(1,11):
a = np.array([])
for feature_name in feature_names:
a = np.append(a, features[case_id][feature_name])
samples[case_id-1,:] = a
# May have NaNs
samples = np.nan_to_num(samples)
V) Plot features as a heatmap¶
In [50]:
import pandas as pd
import seaborn as sns
# Construct a pandas dataframe from the samples
d = pd.DataFrame(data=samples, columns=feature_names)
corr = d.corr()
# Set up the matplotlib figure, make it big!
f, ax = plt.subplots(figsize=(15, 10))
# Draw the heatmap using seaborn
sns.heatmap(corr, vmax=.8, square=True)
plt.show()
VI) Cluster the heatmap¶
In [51]:
# Choose a subset of features for clustering
dd = d.iloc[:,1:50]
pp = sns.clustermap(dd.corr(), linewidths=.5, figsize=(13,13))
_ = plt.setp(pp.ax_heatmap.get_yticklabels(), rotation=0)
plt.show()
In [52]:
import time
path="/home/mrclnndrd/Dropbox/Projetos/Radiomics/Image/"
for i in range(9):
print(i)
time.sleep(1)
image_1 = sitk.ReadImage(path+"000"+str(i+1)+".dcm")
im=sitk.GetArrayFromImage(image_1)
plt.figure(figsize=(8,8))
plt.imshow(im[0,:,:], cmap="gray")
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